Generative adversarial network

As explained in Generative AI in Action

A generative adversarial network is a generative model composed of a generator and discriminator trained adversarially to produce realistic synthetic data.

A generative adversarial network (GAN) creates synthetic data through two competing neural networks: a generator produces examples, while a discriminator judges whether examples are real or generated. This adversarial training is especially suited to complex, diverse data requiring high realism, including images for design, art, augmentation, and style transfer. section 4.1 section 4.1.2

A typical GAN image-generation loop works as follows:

• Feed a prompt, such as text or a sketch, together with random noise into the generator. section 4.1.2
• Produce an image intended to match the prompt and appear authentic. section 4.1.2
• Let the discriminator compare generated and real images associated with the prompt and assign a realism score. section 4.1.2
• Update both networks through backpropagation and gradient descent, repeating the competition until generated images can fool the discriminator. section 4.1.2

Enhancing medical imagery

A GAN can support super-resolution workflows by making medical images more detailed and clearer. The generator proposes an enhanced image, while the discriminator evaluates whether it resembles a real high-resolution image. section 4.1.2

Figure 4.5 GAN model architecture

Use the architecture figure to inspect the generator, discriminator, latent space, and parameter fine-tuning at a high level. section 4.1.2

How Generative AI in Action covers it

How deep it goes

Explains generator–discriminator competition, prompt conditioning, realism scoring, gradient updates, and latent space without mathematical derivation.

Examples

Uses realistic image generation, artistic style transfer, chair design, and medical or space-image super-resolution.

What you can do after

Evaluate GAN suitability for high-realism image generation and explain its adversarial training workflow.

How it connects

Connects GANs with variational autoencoders, diffusion models, vision transformers, prompt engineering, and latent space.

liveBookmanning.com

Read Generative AI in Action on liveBook Get the book at manning.com

Also explained in

• 

  liveBookmanning.com
  Inside Deep Learning: Math, Algorithms, ModelsA generative deep-learning model consisting of a generator and discriminator trained adversarially to produce realistic samples.
• 

  liveBookmanning.com
  Learn Generative AI with PyTorchA generative adversarial network, a model family trained with a generator and discriminator to synthesize realistic data or transform data between domains.
• 

  liveBookmanning.com
  Outlier Detection in PythonA generative adversarial network is a model consisting of a generator and discriminator trained competitively so the generator produces data resembling a reference distribution.
• 

  liveBookmanning.com
  Grokking Machine LearningA generative adversarial network is a generative model composed of competing generator and discriminator networks that learn to produce realistic data.
• 

  liveBookmanning.com
  Sutskever's ListA generative adversarial network is a model architecture in which a generator creates candidates and a discriminator learns to distinguish them from genuine data.
• 

  liveBookmanning.com
  Evolutionary Deep Learning: Genetic algorithms and neural networksA generative adversarial network is a model consisting of a generator and discriminator trained in opposition to produce realistic synthetic data.
• 

  liveBookmanning.com
  Data Without LabelsA generative adversarial network is a generative model consisting of a generator and discriminator trained adversarially to produce realistic samples.
• 

  liveBookmanning.com
  Machine Learning for Tabular Data: XGBoost, Deep Learning, and AIA generative adversarial network is a deep learning architecture consisting of a generator and discriminator trained adversarially to produce samples resembling real data.
• 

  liveBookmanning.com
  Introduction to Generative AI, Second EditionA generative adversarial network is a model composed of a generator and discriminator trained through competition between synthetic-data creation and authenticity evaluation.
• 

  liveBookmanning.com
  Grokking AI Algorithms, Second EditionA generative adversarial network is a machine-learning model consisting of a generator and a discriminator trained through competition to produce increasingly realistic synthetic data.
• 

  liveBookmanning.com
  Build a Text-to-Image Generator (from Scratch)A generative adversarial network is a generative model trained through competition between a generator that creates samples and a discriminator that distinguishes generated samples from real data.

Compare how 12 books explain generative adversarial network →

Related concepts

• generatorhas part · A generator is a computational component or construct that produces data or values from inputs, representations, context, or specified constraints.
• discriminatorhas part · A discriminator is the model component of a generative adversarial network that distinguishes real data from data generated by its generator.
• VQGANincludes · VQGAN is a generative model that combines vector-quantized discrete latent representations with adversarial and perceptual objectives to synthesize images.
• generative modelingkind of · Generative modeling is a machine-learning approach that learns patterns or a data distribution to generate new samples resembling the data.
• DCGAN / deep convolutional ganincludes · A deep convolutional generative adversarial network is a GAN whose generator and discriminator use deep convolutional neural-network architectures.
• generationkind of · Generation is the computational production of outputs from supplied inputs or context, often by applying a model to produce predictions or new content.

This explainer was generated by Manning's knowledge graph from the text of Generative AI in Action. The section links point to the parts of the book it draws on.